Narimene Dakiche, K. Benatchba, F. B. Tayeb, Y. Slimani, Mehdi Anis Brahmi
{"title":"A Hybrid Artificial Bee Colony Algorithm with Simulated Annealing for Enhanced Community Detection in Social Networks","authors":"Narimene Dakiche, K. Benatchba, F. B. Tayeb, Y. Slimani, Mehdi Anis Brahmi","doi":"10.1109/ASONAM55673.2022.10068713","DOIUrl":null,"url":null,"abstract":"In this paper, we propose a hybrid Artificial Bee Colony algorithm with Simulated Annealing (ABC-SA) to address the community detection problem. SA enhances the exploitation by searching the most promising regions located by ABC algorithm. Besides, in order to accommodate the characteristics of social networks, we use locus-based adjacency encoding scheme, in which communities are identified as a graph connected components and Pearson's correlation as structural information to guide the solutions' construction. Results obtained on synthetic and real-word networks show that the proposed algorithm can discover communities more successfully in comparison with traditional ABC algorithm and other state-of-the-art algorithms.","PeriodicalId":423113,"journal":{"name":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","volume":"15 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ASONAM55673.2022.10068713","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
In this paper, we propose a hybrid Artificial Bee Colony algorithm with Simulated Annealing (ABC-SA) to address the community detection problem. SA enhances the exploitation by searching the most promising regions located by ABC algorithm. Besides, in order to accommodate the characteristics of social networks, we use locus-based adjacency encoding scheme, in which communities are identified as a graph connected components and Pearson's correlation as structural information to guide the solutions' construction. Results obtained on synthetic and real-word networks show that the proposed algorithm can discover communities more successfully in comparison with traditional ABC algorithm and other state-of-the-art algorithms.